From business workflows to RL gyms: what makes the data useful?

Begin with a task an agent could actually perform

“Learn from our company’s data” is too broad to evaluate. “Resolve an order exception using the records available at that moment” gives a reviewer something concrete to investigate.

For that task, the useful evidence might include the initial request, the order state, relevant policy, communication with the account team, the action taken, and the resulting status. The value lies in how these records support a decision, not in how many files are in the export.

Start with our AI-readiness framework if you need a way to organize those questions.

A historical trace and a live environment do different jobs

A trace records what happened. An environment lets an agent act and exposes what happens next. Both can be useful, but one does not automatically become the other.

Missing context cannot safely be filled in as if it were observed fact. Mark assumptions and synthesized components separately.

What enterprise benchmarks illustrate

WorkArena++ studies agents on enterprise workflows that require planning, reasoning, retrieval, and contextual understanding. Tau-bench evaluates tool-using agents under domain rules and checks the resulting database state against a goal.

Our inference is that operational datasets should be reviewed for more than readable text. A buyer needs to understand the state, choices, constraints, and outcomes a task would involve. These research benchmarks are examples of evaluation design; they are not endorsements of Ohio Training Data or Clear Harness.

Build a completeness rubric around the intended task

Ask a reviewer to follow a selected workflow from request to outcome. Check the supporting communications, relevant calendar context, shared files, comments, and version changes. Record which parts are directly supported and which remain unresolved.

A closed ticket does not necessarily show a successful resolution. An approval may need an attached decision record. A version timestamp may show that a file changed without explaining why. Those distinctions should survive the summary score.

Use validated linkage to locate the evidence and activity-density measures to describe how much context is present. Neither measure alone establishes task completeness.

Keep future answers out of the starting observation

A completed workflow contains information the original employee did not yet have. If a training or evaluation task exposes the final resolution at the beginning, the setup may no longer test the intended decision.

Google’s dataset-splitting guidance distinguishes data used to train a model from held-out evaluation data. For workflow tasks, our practical recommendation is to review both the dataset split and what each observation reveals at each point in the timeline.

For example, a final approval can be useful outcome evidence while being inappropriate input for an agent deciding whether to seek that approval. The manifest should make the chronology inspectable.

Make the first review small enough to inspect

Select a few connected projects or issues and define the intended tasks. Ask which evidence exists, which joins hold, and which environment assumptions remain to be designed.

Explore quality ranking with Clear Harness, our partner, to discuss the source-data review. Businesses exploring the commercial opportunity can also take the Troveo assessment (referral link; we may be compensated). Quality ranking helps organize the evidence; training gains still need their own experiment.